Identification of Village Building via Google Earth Images and Supervised Machine Learning Methods

In this study, a method based on supervised machine learning is proposed to identify village buildings from open high-resolution remote sensing images. We select Google Earth (GE) RGB images to perform the classification in order to examine its suitability for village mapping, and investigate the feasibility of using machine learning methods to provide automatic classification in such fields. By analyzing the characteristics of GE images, we design different features on the basis of two kinds of supervised machine learning methods for classification: adaptive boosting (AdaBoost) and convolutional neural networks (CNN). To recognize village buildings via their color and texture information, the RGB color features and a large number of Haar-like features in a local window are utilized in the AdaBoost method; with multilayer trained networks based on gradient descent algorithms and back propagation, CNN perform the identification by mining deeper information from buildings and their neighborhood. Experimental results from the testing area at Savannakhet province in Laos show that our proposed AdaBoost method achieves an overall accuracy of 96.22% and the CNN method is also competitive with an overall accuracy of 96.30%

Estado de conservación de la Puya raimondii Harms mediante técnicas de teledetección y modelos Deep Learning en el área de conservación regional bosque de Puya Raimondi - Titankayocc, Ayacucho

Los estudios de la Puya raimondii Harms en el Perú son escasos, pese a su valor ecológico y económico para los ecosistemas altoandinos. Actualmente, su situación es grave debido a las amenazas climáticas y antropogénicas que afectan en el crecimiento poblacional de la especie. Consecuencia de ello, la P. raimondii se encuentra declarada en peligro de extinción, ya que presenta poca variabilidad genética para soportar dichos cambios; además, produce una sola inflorescencia al final de su periodo vegetativo. De manera que, el objetivo general de esta tesis es estudiar y evaluar el estado de conservación de la P. raimondii a través de la teledetección y el uso de nuevas técnicas de detección de objetos como son los algoritmos de Deep Learning aplicado en un área representativa de puyas como es el Área de Conservación Regional Bosque de Puya Raimondi - Titankayocc, departamento de Ayacucho. La metodología implica el uso de herramientas de Sistemas de Información Geográfica y análisis espacial basado en la geoestadística para estimar el número de individuos a través de imágenes satelitales de Google Earth; posteriormente, calcular los valores de las variables ambientales como el Índice de Vegetación de Diferencia Normalizada (NDVI) y el Índice de Rugosidad del Terreno (TRI) provenientes de satélites de alta resolución, CBERS-4A y SRTM respectivamente; finalmente, discretizar la información hallada para caracterizar el hábitat de la P. raimondii dentro del área de conservación. En ese sentido, los resultados alcanzados concluyeron en la detección de 58 607 individuos usando imágenes Google Earth. Asimismo, la actividad fotosintética registrada tenía como valor promedio un 0.23 según el NDVI; de igual manera, para el caso del TRI se identificaron los hábitats más propicios para la especie los cuales fueron suelos rugosos ligeros a elevados ubicados principalmente en los ejes Este y Sur. Dicho esto, la propuesta de nuevas estrategias para el estudio de conservación implicó abordar los conceptos relacionados a la ecología vegetal, análisis espacial e inteligencia artificial.Studies on Puya raimondii Harms in Peru are scarce, despite its ecological and economic value for high Andean ecosystems. Currently, its situation is serious due to climate and anthropogenic threats that affect the population growth of the species. As a result, P. raimondii has been declared in danger of extinction since it has little genetic variability to withstand such changes; in addition, it produces only one inflorescence at the end of its vegetative period. Therefore, the general objective of this thesis is to study and evaluate the conservation status of P. raimondii through remote sensing and the use of new object detection techniques such as Deep Learning algorithms applied in a representative area of puyas, namely the Regional Conservation Area of Puya Raimondi Forest - Titankayocc, department of Ayacucho. The methodology involves the use of Geographic Information Systems tools and spatial analysis based on geostatistics to estimate the number of individuals through Google Earth satellite images; subsequently, calculate the values of environmental variables such as the Normalized Difference Vegetation Index (NDVI) and the Terrain Roughness Index (TRI) from high-resolution satellites, CBERS-4A and SRTM respectively; finally, discretize the information found to characterize the habitat of P. raimondii within the conservation area. In this sense, the results achieved concluded in the detection of 58,607 individuals using Google Earth images. Likewise, the registered photosynthetic activity had an average value of 0.23 according to the NDVI; similarly, in the case of the TRI, the most favorable habitats for the species were identified, which were light rugged soils to elevated ones located mainly in the eastern and southern axes. That said, the proposal of new strategies for conservation study implied addressing concepts related to plant ecology, spatial analysis and artificial intelligence